Design a Data Pipeline for Databricks
Last updated: January 21, 2026
Quick Overview
Design a distributed data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Databricks
January 21, 202622
5
3,572 solved
Design a distributed data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Technical Screen at Databricks. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. Databricks values engineers who can think about scalability from day one.
What the Interviewer Expects
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle a region-wide outage?
- How would you optimize costs as the system scales?
- What would the deployment pipeline look like for this system?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
Functional Requirements
- Data Ingestion: The system should support batch and real-time data ingestion from various sources (e.g., databases, APIs, streaming platforms).
- *Data Processing...
Capacity Estimation
Assuming Databricks handles approximately 1 million user requests per day for data processing and querying:
- QPS Calculation: 1 million requests/day = ~11.57 requests/second (QPS).
- **Data Volum...
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